516 citations · 1.1k across the 9 of their papers we have counts for
5 papers · 1 filter
Towards Characterizing Divergence in Deep Q-Learning
Joshua Achiam, Ethan Knight, Pieter Abbeel
Deep Q-Learning (DQL), a family of temporal difference algorithms for control, employs three techniques collectively known as the `deadly triad' in reinforcement learning: bootstra…
On First-Order Meta-Learning Algorithms
Alex Nichol, Joshua Achiam, John Schulman
This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented w…
Constrained Policy Optimization
Joshua Achiam, David Held, Aviv Tamar +1
For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the rewar…
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
Joshua Achiam, Shankar Sastry
Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards. Recent successes in deep reinforcement learning have bee…
Easy Monotonic Policy Iteration
Joshua Achiam
A key problem in reinforcement learning for control with general function approximators (such as deep neural networks and other nonlinear functions) is that, for many algorithms em…